NBA 2023-2024: High Usage Players with Below-Average Efficiency

Highlighting the ‘negative relationship’ where high offensive responsibility doesn’t translate to scoring efficiency

Exploring the crucial balance between usage rate and shooting efficiency in NBA players. This visualization identifies players who consume significant offensive possessions while shooting below league average efficiency—highlighting a key negative relationship that teams should monitor.

Figure 1: A scatter plot showing NBA players’ True Shooting Percentage versus Usage Rate for the 2023-2024 season. Red dots highlight players with high usage but below-average efficiency (below 57%), labeled as the “Team Concern Area.” Six notable inefficient high-usage players are labeled, including Anthony Edwards and DeAaron Fox. Blue dots represent all other players. The visualization demonstrates the negative relationship between offensive responsibility and scoring efficiency.

Steps to Create this Graphic

1. Load Packages & Setup

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  hoopR,          # Access Men's Basketball Play by Play Data
  ggrepel,        # Automatically Position Non-Overlapping Text Labels with ggplot2
  camcorder       # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

# Get player stats for 2023-24 season using proper API call
player_stats <- nba_leaguedashplayerstats(
  season = "2023-24", 
  season_type = "Regular Season"
  )

3. Examine the Data

glimpse(player_stats)
skim(player_stats)

4. Tidy Data

### |- Tidy ----
# Extract and clean the data frame
player_data <- player_stats$LeagueDashPlayerStats |>
  clean_names() |>
  # Convert to numeric
  mutate(across(c(
    gp, min, fgm, fga, fg3m, fg3a, ftm, fta, pts, tov
    ), 
    as.numeric)) |>
  # Filter to players with significant minutes
  filter(
    gp >= 40,  
    min >= 15  
  ) |>
  # Calculate True Shooting Percentage
  mutate(
    ts_pct = pts / (2 * (fga + 0.44 * fta)) * 100,
    # Calculate usage rate
    usage_rate = 100 * (fga + 0.44 * fta + tov) / 
      (min/gp * 5),  # approximation based on available data
    player_name = player_name
  ) |>
  # Select relevant columns and calculate minutes per game
  mutate(min_per_game = min / gp) |>          
  select(player_name, team_abbreviation, 
         usage_rate, ts_pct, min_per_game, min, gp) |>
  na.omit()

# Calculate average TS% for reference
avg_ts <- mean(player_data$ts_pct)

# Identify players with high usage but below average efficiency
inefficient_high_usage <- player_data |>
  filter(
    usage_rate > median(usage_rate),  # Above median usage
    ts_pct < avg_ts                   # Below average efficiency
  )

# Select top players with highest usage or lowest efficiency for labeling
inefficient_high_usage_labeled <- inefficient_high_usage |>
  # Sort by a combined metric of high usage and low efficiency
  arrange(desc(usage_rate - ts_pct)) |>
  # Take only top players
  slice_head(n = 6)

5. Visualization Parameters

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "#5b9bd5", 
    "#e15759"
  )
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("NBA 2023-2024: High Usage Players with Below-Average Efficiency",
                          width = 70) 

subtitle_text <- str_wrap("Highlighting the 'negative relationship' where high offensive responsibility doesn't translate to scoring efficiency",
                          width = 85)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 16,
  source_text =  "ESPN via { hoopR } package" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(

    # Axis elements
    axis.title = element_text(color = colors$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),

    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "gray92"),

    # Legend elements
    legend.position = "top",
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

### |-  Plot ----
p <- ggplot(player_data, aes(x = usage_rate, y = ts_pct)) +
  # Geoms 
  geom_hline(                         # average TS%
    yintercept = avg_ts, 
    linetype = "dashed",
    color = "gray60", 
    alpha = 0.6
    ) +
  geom_point(aes(
    size = min_per_game, 
    color = ts_pct < avg_ts & usage_rate > median(usage_rate)), 
    alpha = 0.3,
    ) +
  geom_point(                         # inefficient high-usage players
    data = inefficient_high_usage, 
    aes(size = min_per_game),
    color = "red", 
    alpha = 0.5
    ) +
  geom_text_repel(
    data = inefficient_high_usage_labeled,
    aes(label = player_name),
    size = 2.5,
    color = "red",
    force = 10,
    max.overlaps = 10,
    segment.size = 0.2,
    segment.alpha = 0.6,
    seed = 123
  ) +
  # Scales
  scale_color_manual(
    values = colors$palette,
    labels = c("Other Players", "Below Avg TS% & High Usage")
  ) +
  scale_size_continuous(
    name = "Minutes Per Game", 
    range = c(1, 6)
    ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Usage Rate (%)",
    y = "True Shooting Percentage (%)",
    color = "Player Type"
  ) +
  # Annotate 
  annotate(             # highlight high usage and below average TS area
    "rect", 
    xmin = median(player_data$usage_rate),
    xmax = max(player_data$usage_rate) + 1, 
    ymin = min(player_data$ts_pct) - 1, 
    ymax = avg_ts,
    alpha = 0.08, 
    fill = "red"
    ) + 
  annotate(
    "text", 
    x = median(player_data$usage_rate) + (max(player_data$usage_rate) - median(player_data$usage_rate))/2, 
    y = avg_ts - 12,
    label = "Team Concern Area:\nHigh Usage, Low Efficiency",
    color = "red", 
    fontface = "bold",
    size = 3, 
    alpha = 0.8
    ) +
  # Legend
  guides(
    color = guide_legend(title.position = "top",ncol = 2),
    size = guide_legend(title.position = "top", ncol = 3)
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.4),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 10)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 16, 
  width = 8, 
  height = 8
  )

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 ggrepel_0.9.6   hoopR_2.1.0    
 [5] scales_1.3.0    skimr_2.1.5     janitor_2.2.0   showtext_0.9-7 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3
[13] forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2    
[17] readr_2.1.5     tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1  
[21] tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1    farver_2.1.2        fastmap_1.2.0      
 [4] pacman_0.5.1        promises_1.3.0      digest_0.6.37      
 [7] timechange_0.3.0    lifecycle_1.0.4     rsvg_2.6.1         
[10] processx_3.8.4      magrittr_2.0.3      compiler_4.4.0     
[13] rlang_1.1.4         tools_4.4.0         utf8_1.2.4         
[16] yaml_2.3.10         data.table_1.16.2   knitr_1.49         
[19] labeling_0.4.3      htmlwidgets_1.6.4   curl_6.0.0         
[22] xml2_1.3.6          repr_1.1.7          websocket_1.4.2    
[25] withr_3.0.2         grid_4.4.0          fansi_1.0.6        
[28] colorspace_2.1-1    future_1.34.0       globals_0.16.3     
[31] cli_3.6.3           rmarkdown_2.29      ragg_1.3.3         
[34] generics_0.1.3      RcppParallel_5.1.10 rstudioapi_0.17.1  
[37] httr_1.4.7          tzdb_0.4.0          commonmark_1.9.2   
[40] chromote_0.4.0      rvest_1.0.4         parallel_4.4.0     
[43] base64enc_0.1-3     vctrs_0.6.5         jsonlite_1.8.9     
[46] hms_1.1.3           listenv_0.9.1       systemfonts_1.1.0  
[49] magick_2.8.5        glue_1.8.0          parallelly_1.43.0  
[52] gifski_1.32.0-1     codetools_0.2-20    ps_1.8.1           
[55] stringi_1.8.4       gtable_0.3.6        later_1.3.2        
[58] munsell_0.5.1       furrr_0.3.1         pillar_1.9.0       
[61] htmltools_0.5.8.1   R6_2.5.1            textshaping_0.4.0  
[64] rprojroot_2.0.4     evaluate_1.0.1      markdown_1.13      
[67] gridtext_0.1.5      snakecase_0.11.1    renv_1.0.3         
[70] Rcpp_1.0.13-1       svglite_2.1.3       xfun_0.49          
[73] pkgconfig_2.0.3    

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_16.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data Sources:
   • ESPN via { hoopR } package: hoopR